{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 利用PaddleTS进行时序异常检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个notebook中，我们将介绍如何使用PaddleTS进行时序异常检测，主要步骤包括：  \n",
    "1、导入数据  \n",
    "2、数据可视化  \n",
    "3、数据预处理  \n",
    "4、模型训练  \n",
    "5、模型预测和评估  \n",
    "6、模型结果可视化  \n",
    "7、模型保存  \n",
    "8、模型加载及推理  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1、导入数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以直接使用PaddleTS集成的公开数据集，也可以导入自己的数据（如何导入自己的数据请参考TSDataset相关使用文档） \n",
    "\n",
    "在这个例子里面，我们使用NAB中的一份温度数据集，NAB是一份比较经典的用于时序异常检测的数据集，详情可参考https://github.com/numenta/NAB  \n",
    "\n",
    "NAB温度数据集已经集成在PaddleTS公开数据集中，可以直接使用下面两行代码导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from paddlets.datasets.repository import get_dataset\n",
    "ts_data = get_dataset('NAB_TEMP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据导入之后，可以直接查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     label      value\n",
       "timestamp                            \n",
       "2013-12-02 21:15:00      0  73.967322\n",
       "2013-12-02 21:20:00      0  74.935882\n",
       "2013-12-02 21:25:00      0  76.124162\n",
       "2013-12-02 21:30:00      0  78.140707\n",
       "2013-12-02 21:35:00      0  79.329836\n",
       "...                    ...        ...\n",
       "2014-02-19 15:05:00      0  98.185415\n",
       "2014-02-19 15:10:00      0  97.804168\n",
       "2014-02-19 15:15:00      0  97.135468\n",
       "2014-02-19 15:20:00      0  98.056852\n",
       "2014-02-19 15:25:00      0  96.903861\n",
       "\n",
       "[22683 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2、数据可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据后，可以通过PaddleTS提供的可视化算子展示数据的具体情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#导入可视化算子\n",
    "from paddlets.utils.utils import plot_anoms\n",
    "plot_anoms(origin_data=ts_data, feature_name=\"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3、数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在正式进行建模之前，确定训练集和测试集（在这个例子中，我们用前15%的数据进行训练，后85%的数据用于测试）  \n",
    "\n",
    "之后我们对数据进行标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from paddlets.transform import StandardScaler\n",
    "# 据集拆分\n",
    "train_tsdata, test_tsdata = ts_data.split(0.15)\n",
    "# 标准化\n",
    "scaler = StandardScaler('value')\n",
    "scaler.fit(train_tsdata)\n",
    "train_data_scaled = scaler.transform(train_tsdata)\n",
    "test_data_scaled = scaler.transform(test_tsdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4、模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完成数据预处理之后，我们就可以进行模型训练了，在这个例子中，我们采用AutoEncoder模型对数据进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-12-08 10:48:52,417] [paddlets.models.anomaly.dl.anomaly_base] [WARNING] No early stopping will be performed, last training weights will be used.\n",
      "[2022-12-08 10:48:52,686] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 000| loss: 0.322022| 0:00:00s\n",
      "[2022-12-08 10:48:52,920] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 001| loss: 0.085059| 0:00:00s\n",
      "[2022-12-08 10:48:53,154] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 002| loss: 0.064829| 0:00:00s\n",
      "[2022-12-08 10:48:53,410] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 003| loss: 0.052350| 0:00:00s\n",
      "[2022-12-08 10:48:53,641] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 004| loss: 0.047867| 0:00:01s\n",
      "[2022-12-08 10:48:53,896] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 005| loss: 0.045785| 0:00:01s\n",
      "[2022-12-08 10:48:54,145] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 006| loss: 0.041525| 0:00:01s\n",
      "[2022-12-08 10:48:54,378] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 007| loss: 0.040773| 0:00:01s\n",
      "[2022-12-08 10:48:54,617] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 008| loss: 0.040255| 0:00:02s\n",
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      "[2022-12-08 10:49:09,318] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 069| loss: 0.027064| 0:00:16s\n",
      "[2022-12-08 10:49:09,555] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 070| loss: 0.023956| 0:00:17s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-12-08 10:49:09,798] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 071| loss: 0.024286| 0:00:17s\n",
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      "[2022-12-08 10:49:16,255] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 097| loss: 0.022406| 0:00:23s\n",
      "[2022-12-08 10:49:16,490] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 098| loss: 0.023066| 0:00:24s\n",
      "[2022-12-08 10:49:16,727] [paddlets.models.common.callbacks.callbacks] [INFO] epoch 099| loss: 0.022656| 0:00:24s\n"
     ]
    }
   ],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "\n",
    "# 固定随机随机种子，保证训练结果可复现\n",
    "seed = 2022\n",
    "paddle.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "#建模与训练\n",
    "from paddlets.models.anomaly import AutoEncoder\n",
    "model = AutoEncoder(\n",
    "    in_chunk_len=2, # 样本数据窗口大小\n",
    "    max_epochs=100  # 最大epoch设为100\n",
    ")\n",
    "model.fit(train_data_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5、模型预测和评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型训练完成之后，可以对测试集进行预测并评估  \n",
    "\n",
    "评估的指标采用F1,Precision,Recall，PaddleTS已经基于TSDataset实现了这三个指标的计算，直接调用即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-12-08 10:50:39,186] [paddlets.metrics.base] [WARNING] Tsdataset true's and pred's time_index do not match, the result will be calculated according to the intersection!\n",
      "[2022-12-08 10:50:39,208] [paddlets.metrics.base] [WARNING] Tsdataset true's and pred's time_index do not match, the result will be calculated according to the intersection!\n",
      "[2022-12-08 10:50:39,228] [paddlets.metrics.base] [WARNING] Tsdataset true's and pred's time_index do not match, the result will be calculated according to the intersection!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1:  0.4904181184668989\n",
      "precision:  0.946218487394958\n",
      "recall:  0.33098177542621987\n"
     ]
    }
   ],
   "source": [
    "from paddlets.metrics import F1, Precision, Recall\n",
    "# 预测\n",
    "pred_label = model.predict(test_data_scaled)\n",
    "lable_name = pred_label.target.data.columns[0]\n",
    "# 计算评估指标 f1, precision, recall\n",
    "f1 = F1()(test_tsdata, pred_label)\n",
    "precision = Precision()(test_tsdata, pred_label)\n",
    "recall = Recall()(test_tsdata, pred_label)\n",
    "print ('f1: ', f1[lable_name])\n",
    "print ('precision: ', precision[lable_name])\n",
    "print ('recall: ', recall[lable_name])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6、模型结果可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以将模型的预测结果和真实异常做对比，更加直观的查看模型预测的效果  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 预测值与真实值\n",
    "plot_anoms(origin_data=test_tsdata, predict_data=pred_label, feature_name=\"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在异常检测场景中，PaddleTS除了提供predict接口预测每个时间点是否异常，还提供了predict_score接口获得每个时间点的异常分数，分数越高，表示该点越可能是异常"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MI2Us3BA6q9Tq7ZWUTJzK8Ps/8XG8deLVeRtiKnuySSuhUSn1c6VUR6VUnhl36Hml1C6l1DilVG+l1ElKqd3msUopdY1SqqdSaoCTA4wTIkKOS7h2SGGATcVFI7sxvMSYSgg2/qk1v1WWubzTp71WJCqhIRhOXm69Iwg4Go5IBRL7aCgaIcbu7Tbs8FZ8uNQY3bYtcrZhmX2L8zQBJEbbGYqq2no27TnIhl0H2V9VS129O+yI0fL0/mBxoGJ70rkDoi7Dks17+c8Co7P8dPl2/vS/Zbzw5TqUUtS7VcJDbYz/uxG+Jpb83wccgtnOWrmTLRWH2Lg7VB4lL5c7eFxbZhJO5Do4fFmejKE4aEZKmGqGqLr2tW+N69lswSzhs7hpHpXVdZRMnEpNnRu3W/GPstWeaAvfb9nLC7PXhb1nMrj57UXc+s7ilNxbExqnd3OPOT36909W4XYrqv2eVbtg5HSdgtwcPrrhBABe+r8RNM33uh5MPK0vAAdq4htUvnzFCK4fF2j6kUxuGd+Xly4/OmD7b19dyIB7PuaVuRu48c1Fnu2HbN+c35zQA4Am+V6HnCPaFzl+Hz5eZgjB+UFsRx+7cLDj9r+f6P3e/m3GSg7V1tMkz9kBaFDXYrq3DfRLOfcfX7HnQOD0OBjhfj5b7hXQMzV0XloJjelAnvlBCW7TaDykc9zGEd/VG/+vrTrA5VUH4o4jZ2f6Dcf7rE+5ZjR3n9kv7uvus2lUrHs0zQ98OapsIQdOjMLmcadtGrCrTZV/39nOZbePFq9+dYHPlMWMJOfhvu71bznuoZmc8MhMzntmDr1u/5Axj5RRMnFqUE3rda9/67P+y2MO9yyf1t95KtXtVgy//5OwoYWWmYONrXur6H7rNHreNo3ut04LqY1rSOwdufW7PfvFWo6d9BnHPzwz7PmRxkVceOfJHs1KnoNDmr95hRPBBjp/OLWPx8jcwq7Bv++DZTz7xVoenr6CI+8yHH5ue3cp936wLGZt5IHqOsfAxp8t38beg7W8v2iLTzgwO2/N38TrXzdcisOaOjcvz10fdyD1xs79cw/R/dZpAWYr9v7L6Tm98c1FlEyc6iMo7jvkOxjr06E55ZMmMOYI36nWy0eXeJYf+SjQzt1i76HagEGcPaTP8b3bGQ6KJnOTmFbQEvjOHNQpIufABz/8gbe+2Ui17f0d3LXYs2wJayJCfYjvbUGes3jj36YWxQXe479YtZO9h2r5MoQNajCsDGEAb36zgR63GgPiQfd+zAPTvL9ZsjW8yUILjX5Y01X1QZ5F6zHuJkbTWWPASkABf6xOnAOEv2f0wM4taVOUH+ToQA7V1Dt2/FYWjMcuHEzfDi24fHSJj/bF4gxb9P0f94Z27FmxdT+3vbsEt1v5ePVN+c4rJB3bs61HU/rf33odfez1nLZkK6u3V3rWf5NkZ5gZy7wv7vKtvtPjA+/5OGjGAPsU65mDOnmWW9qED7vGdc2OSnZWVgcInHZKJk41veQNW9B0xIpdVtqnnccQfXsUTl+XPP+1Z3lCCFvF1jZvTidNo/Vh3lVZHVTDaRfC7B/yCQM6Bswy2AcuNXVu3rLloVZKscgUDJwEu237qrjpze9CasXHPzYrILDxN+W7+b/J8xl078f87vVvuW9qoIOP/ZqfLd8Wc0aNSKg4WMOt7yxm2H0zuHPKUv5ja4M/T13GzW8bWqD3vtscsVY5UtxuxdodleEPTAOWbNrLiq37PSGh/B0S7ZrFS54PHhdw9KTPPMtzIhTaCnK9g/unZq7xLO89WOt5Vg7W1DHoTx97BnFKGbMVodKcXvjs3ITPaJTvPEDJxKn8088MZs0Dp4c875+fr+Xm/y5mhG1q2i7gzvxDqWdAGUpJk5/jcpyZaxFBxBEL+7fIiYd+GjizZM0y1ta7ueW/Swg19lq4YQ+/f2uRj1NquqOFRj+scAjBHGGsrWNyDHGxld/IaVeQ82KluS0qvcsltG9u2FlY261pukuen8f0pV4jZ6UUR9413ROJ386slYa2x/oAlu88wL6qupingpVSnPr3Wbw2bwM/7qsKmI6xaNk0jxk3jaF80oSgnm9A0PMjYcXW/ZRMnJqwj1qfO6Y7dhz2KdYhtlGwnd+/9Z1nuS5I2/pnKLD4pjzQNiaYJipWgk3fhOLrdYZdYP9OLbnxJGN6K9yAIhhP/nyI4/YRJa191vNCTE8f82BwDedBW3vZ0411bd3U5yMEUNzUK6S+OX8jrWzr9oC/hxy0lyMf+JR3vt3MS3PKHcsBODqD/dbPWeq1eRvYc6DG5+NtzzD0f5PnM+z+T1jsl9UmUQy+dwavf72R/eZgx659+tcX63hrvjGtev0b30WkVY6GEQ98wthHP2dpjI4iDcmZT87m1L/PctynlOLkv3odF53eY4sttvemlTnYHNilZbDDg7KvqpZB937MI6aN9dlPeoXY6rp6bp+y1OcZDsZjCU5KUBpkdirHJax94HT6dmjuuN8J//fV4vELnfsQML7lR7QPvIcrxlBFTlxwdDfKJ01g2b2n+mxXSkXk9HTuP75KiGNhQ6KFRj9yw0xPW915E1NY3KDcjiO0YCrwaPnurlN44CcDPKMzl0t4+GeDPFOi+6tqqXcrvli1k6teWcDM5du56uUFrN9lCE1OoRnuNUOWzDMFgJkrDCFyXRhbvmACzgM227h9h2rjduK4/d0lPpomO/d/sIySiVM9aeX8sTrz377qrKF0uxUlE6cy1cEeMRhWZ+qkjQWCxl2zTz/YtY52rdeGKITbI++antCRaNOC6AO2b9xjlLfXYUVROWVd9/q3PmV/+uKhiAjL7/PPGgr/vmKEz3quw/S0pYkNNU1tF/D8bSAH2DzdHziuCcVNfOuyYL3zx/5dMzzJnDW7AjQDTuFUPv1hW9DfbGdloO3TkPtm0P3WaUz87+Kg79FvX/HNCPV02RpKJk718TRNBANs4bAsftybnFBSVlskIuNJKrDMd/4zf1NQzdIfT+0T9Py2puY72vijNXVudpttZ9nIrbINcvvcMZ3X/CIUFNmCpFvOMGDYYCaCSqW4oUPovsHlEh/tfjh+rHB+tk/p14Fv7zzZs+4f/sZ/inrOrWMjvifAn86KzBzMbncKxkDTPxJEY0ELjX5YU2HhxJ4qU1B8u64Wp27ujglHJqQ8OS7hopHdAgK5dm9rqN33HKz1iWt3+eRvmP79Vp98mCv3+NbGmpq7aGQ3n+3BPpQW5/zDOS7ke7Yp6NMe+4Kny9Y4Hhcp32/ZR20QbeNzpjNCuOnbpZv3OU7Nv2lOuV3z2sKgKeTOsk03g9fx5elfDOPXA8KbB8z6Y6BjzyO29HWrtlWydPNeHpq+nDOfnB1wbDiCTZlHgl143XeoNuopKWtq55iebejaOrLMLCUTpwbYcp42wJiaLszL8Ql7UT5pAoV+xuf2Z7+FqWFfsH6PjwDz5GeBH7xNewIF8ttPP9JzTet+nYpcEQvAVopIp0GJ3dQBjBRwV7zk658XaXu/8c1GLnsxcJYACIiz99B0w07quIc+czo8IrY6aIs7FxvufvYZiGMe9N7DKR5evCTSJjwZBAsRYwm7N//X2WHpf9cexzUn9mJtkKnZWKs9dckWqsz+wFIUhGPpn7xasT+cElyQjZVrqyMrR7DZFydCzWbYnWNGdPedpbBnujm+d1s6towuheKlx5ZEdXw2oIVGP/JMrVEw/YX1mB9uaj9Oz8ljkwo8ureDWjyRWKPF294JzOMM8PlKr8PBc0uqufLf8ymZOJXt+6u41NRS+k+FhJueXrfzABMe/yLAO8w//p1Tx3r9uN4hr+3P/upAL91ocZrqutXWXte/+Z3jeY//fIij3c2uympGdw4vXHRrEyhMfWebUjz98S8444nZMQvXNwYpdyS8a5sKcavovTDvN+3unBynnFjpEEbJ7jgUCXabxjvM2Gz/KFvjI8D85ePAvL5O7RtMm+6fESMcTjEdj+roG2ar1+0fBhxTVRu56cXctYEhgiysd9XuWFQbzBA7DPVuxagHPw2638ljHuDSFxOfd/yOKfEHjk8mTs+zxfb9wQWbAWZf6zIHK62a5tGhhT1lpfHb+afzC8crczcEjYMbCSLCi5cFejQ3BJH2IQAXj+oWdF+Bber6H37vvN0GOJKIC6nGyRQn3dBCox85EU5PW6JDvqQmF6MlNH7tEHvOn+0HlScMwYg/f+pJsG7ZtFmBY5vk5zDPtGOya+HsUybfb9nHEDMws4V/8FT71J/F8b3bhi2nE338hO/TB3TwLIfTNs5cYTir7D1Uy5JNgQLkIj+vR4C7TKHEKUVXl1aGMDiu72GhC+3Aqf06hNw/qEtLxyCzTqm2pi2JPej5ne9977Pe/+6PfGxhw2GNK4Klr/QfeNidqSz+PSe6wMQ5tg9pMBMJJy4eGfihufbEXo7HRmMcH4zx/UP/xgCfr9we9phIsMxO7I5FFuf+40tKJk4NKeDYWbfT2djfMp0INu29Lg7HFaUUUxf/yI1vfuczyNxfFf9gMZFU1db7PNNOphIWJz5SFvF1j+nZhiKbvbp1i0hkRrt9X9dWTThoE+rDmQY5aTpPjKE/i5Tv7jqZi0Z243/XHhewL5zQeKQ5CJt767ig4drAN0Zup5aF/PX8QZ733B7t4GCUA+Rohem7/IKNB+PokkB7/qcuGmoGDz894BvgH8Q81Wih0Q9LkKoL4wjjQsgB6hRsdPuOYH67O/kdX2UcmjgrbZT1sllTfte/8R0XmF50W2xTYDePDz2FMbBzSwpttiOPzjC0Pg/8xOtZNtzPucGfcwZ3ctxe69e2dm2Ov12j/3S0ZaMz6E8fc+aTsyOa1h3dyyvc2m1lwOth+9ylw3n2kmG8+quREefkPrJjaM3zm785xrNcPmkCH91wAt/cfhKnRSCIRIPTR+mqVwJTizkRydSqv6OI0+i+Y8vCgG2hsAvwTlrcYGzbZ2gZvrn9JB49bxBvXDmKTg6xT/2ZfsPxQTtqlwRvh48iyGAUSuiIhl86OLgBzFu7i4VmQOBT/jbLE8IpFK/M3eC4/YFpy1FKMeFxZxOKn48Irv0JR/dbp3HNawt599vN9PbTyKaLB2lNnZu+d073TP8DIdPDBtPad3Mw43CJ+Aij1iMlhJcap/3OG4rtPT+zj753BnpI2zMqJdIJJBKKm+bzwE8GeDStdixPcP8MLhaPXTiY8kkT6BBFf3HagI6cO7QLfzDtR+0ObZFU3TpmwR0nRS1M/59NuTLeQUnw1m+OoXzSBP5z1bEB341gPok/3Ds+ajvXZKOFRj8sw9naMN9HwdAw1qJ4qc53uvaImuTb5TgF/o4V/3APuw/U+HiC2sM8OHGwpt7n5bT42bAuDkc789fzBztur3crPlu+zeMN7W8H89ePV3hGk/7x0vwJFXICDCPqXrYQDf5TllbHJyKc0q+Dj4DpjzUdbzlLPGyzafTn1H7tA+z4+nRoTrvmBZ4QN/7EGh7DOs1fgzt60mc+YR8um37As2zZrjl5YP7jYiPeoaX9+HDpVnZWVvOf+RuDfvytAMWRYhe0TgmTB9bOkzNXe8r202FdGNUjdI7Y5gW5tGmWT98OLbjiuO7Mu22cR8juaaYeHX5466De/ZEE9P/Vv+dH7f384fXHB93nH7bozW984zme/vgX1NS5ueCfc1i+21moedeWe/jW0/r62Hde9uI3Qevb32FGIRRKqYjDipRMnMqOEGkf4+HL1TsjchqyNHivfe0Vqu3RKPq1cQUNFA3w7CXDAHj4ZwMD9uW4fGMMKs/0dNhi0euwIo/GUKnw2sVIoyTYHWQSQbiczdZMkD3Sxdi+h/Gr47pz39n9HD2fw3GpX/aoKGf76dHO6P/tWuBoOG9YF4Z2K+YZ87cHQyFyWv8ODOrq+76EEgaX3HMK710z2sdeM11IxcxqWlOQY6UHdMb+qc7Fm1bQ4ghxRTBWjJ+OxdFpa0Ix5oh2Pp5z7yzc7PH8tcKq2PG33QoWOT8a+wynEfC5Qzozb50Ryy4/18WK+8ZT6Td99fhnq1m6ZR8vXHY0a+KM8/bX8wc7TkuDs7YgFMFCRNhZdPcpHKypC2mc/eZ856DO2/ZVRzUC9+f0AR0jTts4+N4ZQfedPqAj5ZMmMOXbzdzw5nf84T+LHI+bfcuJHPeQEablxS/LOXtw54jLmmN7jkSE1X8+jfowcefsBPN692fhXb6a5fYtCrn0mBImf1XOmYM6sXBDBfsO1Tpqc1oU5tIzSLamP57axyd93FlPfukToqN80gSPIDX31nEc1ryAdbsO0L1NM897UT5pAnX1blbvqPRk9AE8UQBGdm/NvHW7PbMIdo64w9DkzVsHV50bWD5r0PXkRUM4Y2AnfjqsC8PNmJJ22+gvbj7RJ9ROuLzB/ljRGiJl/N9nscBP2x8vSikufs6InRhulsAjLDuMz765/SS+XzCH0sGdKczL8Yknm+sS6tyKYYe3CnqPHBGfmRFrQiVSIcfeX4YKxXXTyUcwqGsx73y7mVE9gs/2jOjeOuHfrXDOoJYpgr0d8nNcHrvlWAgU9qKr1Wu/HsnC9RVhFSXBeOS8QZ7lyZcfzZvfbORvFwx2DGp+5xlHMWPZNjbsPhiwv3lhHoOChHJLNVrT6IdH0xhmelqAHHxtH2/OK+S2gsRpAEMRLL2RhWUfZ88gEIySNr7pkOzp5Xq0C0yVdFgLX/uSbzdUOIaOERFHO71gPH/pcN67ZjTnDevC/DtOIi/H5Zk2ralz8/zsdR7vaTtW2r+b3zY8F+12j9HgFAJi+g3Hc8pR7fn8j6VRXSsSgbllk7yovfksRj34KRc+Oydg+2OfrOLa1xZSW+/2jOAfnr6cBev3+EyH/ba0Z8C58VAYJPuCRZdWTT1mEGcOcjZFCIa/0Jeb44qoUz+ivSHERWpwn5fj8jjCWXxqpv1avGkveS4JKijtq6rj2w0VlEycylF3TffJx/1/o7t74vBZPPShczaPDi0LcbmEnu2KAgZSuTku+nbwDtjsU//RCmROWLFTg9mPdW3dlBcuG+6Z7qytd/Poxys4PUj4K38ufHZuVOXZFSQdWzyEipvoj6Xld3LKa9HEK5ycdKSvRs3SpDcLoblzufymp32+LNFxyGE2Yvl94/n6tnFcN7aXJ6avU9o7i7wcicqbORS5wAkH6jm8TfD7Abxx5SgA/nfdcfz7/4wwW/06tQh1Slj8v4t2WSyS2h3WvDAi2+RIKO1zGE//YljILDhWH5X+7i9etNDoh6XKj8TcPgfx8bLuniB7pUjwn860Y3eesIdUeOzCwVw1JlBYCHWtCWZoFPu04MFq35Ht5opDAR1OqCm1YIw7sj2DuhbzyHmDaFtU4KNhAm8OYcBHiPOfqR3Z3TsN6fSR93fcsXCaCujboQXP/nJ4ROmv7CTKdm10L6Mu+bkurjiuu4+n4Ny1u7n+Dd8MM3/7ZCUfLP6R3rd/yPEPz+SNrzfwj7I1/PTpr6i0Gczn57oonzQhaAiQULz+61EB20J5+jYz2/UqU1DtH+WHIVKPUn8nnKHdWtGhRWHUv50dyzN11fb95OW4fJ6nm8f34YXLhvPZ78f4nHOwpp4B93zsWW+Sn8N71/g6Arzk5wz02e/HRG14v2Lrfk7t197nmbBYcs8pjuf4hyGyr9sHMPNuG+d4/ti+7T2pTGvqFU98tpplPzqHtwrH6F5tAswkoh1QRMvHEdidWoSygbYPWvxnJ644rjuL7zklZL+a45cCz1qMxeTwgJ9QO+aIdhTm5XCY+ewP7daKJ34+hDsmBNfg5bpcCREa65WiDticF74io3q0oXzSBJoX5nHCEe08YYli4ZObTuAv5w0K+C3SXRjz/u7pXlIvWmj0w+UScpQKatNoHw+68E032KQBf3j/l8OuzSv7Q6ln2T7aPenI9kw8rS+L7j7FR1jw1xLZDdwtbcdjFw5h0rkDaNMsP8Bju1PLQo4uacX7145mcNdivr59nMfzLR78NUz2HMT2qdl6pXyytthtaT508Ax+x5bC8NHzBvHML4YmXPMWTNP44fXHM7pXm4i1r6NMAfiJnw/hzjOO8ozILd77bktI+8aJthBD9ebDav9Q+2uzupi5wguDfO8uPLorx/gF0A1FSZumfHe3IcD85oSevHP1sYwMY1voT6TTy/6Bvqvr3BGZCURChxaF5OW6fMLa/OaEnozt295jBxWK1kHSf547xJim79GuKGrD+zOfnE29W9HrsCLa2q7fr1MLmhfm8d1dgVO7j/qFJrJMBvxp3yK46YNnYG3TdDoF/d6+r8qjcX39a19nmxcvP5pXfzXKZ3D5f6O788TPh/C9LY6gf3iveHGaqQiGv4YxkqQFVtD6cN74Lpdgf1w9jjAxfEN2HfC1/XSahTpzUKeQms9cl1AXpbmBE1aLdQ7nFODAgC4tY3bU6XVY87A29OkolllxSTNIZtRCoxN5KsLpaQmebjAVWFPB/p3DfaOb8IdTjvBsb9kkz+flFBHev3a0RyBp6/CBa5Kfw4UjujlOGeXnuujYsgkDuxQz5ZrRHNY8MfaW/oKxPV6ffaS/ftdBTrKl7rJ7yH69ztfJ5+GfDvTpmLu1acr4/h25ZXx0zhnhsLLENPezsTmyYwte/VWgpi4Yvy3tyVMXDfVoep0ELstBJZxzjJWu6hdBYp6teeB0Zt8ylvJJE3jm5GaUT5rgEwgYYNJPA436Ac4ZEmijuOrPp1H2xxM9U745LkPrES3BPiQ/3OubTeYsU4iyWLypwjFodTR8e+fJtC3KZ/LlI8jLEWrq3PQwp/mC2b/asTJUBHMyODaEM1Uw7E5wB2vqaZqfw8TTvPZjp5uzA8VN8/nkJl8t6LsONo/Rkpdr1HvVdq9NbMXBWu6cspTHTNvovYdqGfHApwy452OUUj7xUdc9eDon9jEEZJdLGGzabl04oivgO9D1D+/VkNgzelTV1jPu0c+DHmt5y4aLEmGR4/INZG5NT8ciO7z+ta/d83CHkC7hyM0R6mKM82nHEjuLE5vxNCGkz5c6EC00ZjhVLuHj+vAhbWoUbE+DDAaXhgmW3LW5i2vHhg6uPbBLsSeWYiRhOvZV1TJ18Y9s3H3Q8+FKNP72ZcHS9TlxySijTaxwIqf2a0/5pAmcf3RXn+NizbcdDks7Zn88DnOIuRj2OjkuJgzs6CPoBptSnrUqMGWknfunGraqOUGmzp2EoKKCXMonTWDOrWN9NED+DO5azH1n9+NvF3gNwf1/v0TTJD+HRXef4jG4X7mtklvf8WbkWLPjQMg0g5HQqlk+8+84mWYFueTnuKhzu1nrkEnoy4lj6duhOT8f4ft8veYwlW8nFvvbv9iM7Q/W1NMkP9ejsQR8UnD2OqyI8kkTfNKhWV7J4RxZnvnFUM+yffBjmV7sszmlnfHEbF6eu56/fbKSRRsr+LUtE84bfh7d/tq0d68+ls//WBqTt2w8rA+TNtVO+a4DIcNSPXPJMMonTXCMq+qEvyOMV9MYcZGCclkMWUyM6enEBb9OFxko9V/nyEgDMSJitNAYJZ7QCEAFirUO2WAaCsuWKdLRbThEhPwcF58uDx+A+Pxn5nDNaws5/uGZHKqtD2m/Eyu5PtpQyItAs7PInAr1F1j+b7RveIPHf24kuu+bgGl0JyytqF37FyyfdrS4XMK6B30Fx+q6ep6dFVmGGSvUhcWCO04K6+jTsWWTkNNbAJccU8JPhhhTRIlKoxmOlk3yfIQSp3zOicKwaXTu3TsXN2H6DSfw4LkDWWzaE0YSFisWL03LPODko9pzqKaepnk5PtpYp/BX9uDyR//5E2rq3HxiS3t4whGBsfLG9/eG87nv7P6eZcv0oiqI1+7ZT33pY8Ji1zL6a4fB6Hf8nSbevDJybXw02FOnhsrOs9tvRsVuo5yIcGc5Lpev0Gj+j8a27Q1bGw0/vBUXjezGkxcNiWpwbZGbIEcYq0XTRWi0k46CWTz21qkiY4RGEblRRL4XkaUi8rqIFIpIdxGZJyKrReRNEUnIV7ljrWKQy7kzT6fnzprecDKEjxW7LV4ozdjyrd6pqUM19UmJJ2Xv/JSCMlv4j2BYHtCX+Glf2/hNuZ81qBPlkyY4ekwnAusj41ZG7Dtw/pjHioiw8v7TPOtVNW6+XL0rxBleNvp5urcpKgjr6RgN5ZMm8KvjeyTseuGwm1P4B1Hv6eD9Hyt5OS6PHd/I7sEHai0K8yifNIEvJ4712e5kaxbJFLcT7VsU0LppvuGg4/f+Ozlw5PrZ2D7x2Sp+++pCz/rkME449j5GRMjLkagzbICzs5kTQw/3TrHGk2vdH7vH+YMf/hD0uL/O8I2ranc2effqY/0Pj5ocl2+oGbc3unfE2GdJ5q/fwwM/GcAZA2NzJMp1uRIyPW2ReaJQaklHgTYYGSE0ikhn4HfAcKVUf4xoNxcCDwF/U0r1AvYAVyTifnlKBU8jGP27nTSsPiORWj77B8g/+Tv4jtQt6tyKbzdEHsoi4rL4fVD9PTTn3urr4flPW0BV/xAnvQ5r2KkvS/iuV4qepqNEop3r83NdHnuwaKZhbzo5MPZmJnPtiV7Ti6dm+mpb1+yIfAoyHAV5Lk/Gm1hC3Hx84wn8+SdejV2PECFQwlFZVceaHZW4FfzPLyuIk7Y1z+/he+Kz1Z7lwjxXWAcEf+E2L8dFVZTC3IuXR+4dbp8piDQep8Vadz27g8wA2YXGshXBB6H+WXLsMU0PC+EkFCku/+DeMXxX+nRIXJ9mxJaMf9Ys3WSfdBfGLJvnaDJdOWEq0b4WkUWmcu1PiSifExkhNJrkAk1EJBdoCvwIjAXeNve/BJyTqBsFSyMYjNNzkqOxCoUlRCVUaLR11k7heUodprEgdMiVWAk3zeIf3NoeLy0RuYTjwbIbVEp5tDyRpAiLFsuGLpTQ+JMhnTnO5nBRkARTglTi7yEda7acsPfJiS8sSdfWTbl4pFcDHo92/kBNPfPXOw/ULA94O/6aRjsfXBc+PJa/uUdejivk9K4TwfqORHNvTRU3VR/ixdpqLqs6wL9rDRvOldv2876fgB0OK7KEFQM2UeT4pRHEkxEm8j4ikbM7iXKE8abZTT9U2om0RnimLyeOTYQ9bzUwVik1CBgMjBeRpNh4ZERGGKXUZhH5C7ABOAR8DCwAKpRS1rzBJsAxzYSIXAlcCZCbm0tZWRmVlZWUlZU53i+3GKqpp7J3YIaRql3AJuFQz4Pwg/cFH9G7hsoCc4RfQtBrJ4PvF3/LwfXBO5BQdfXHXevVUmxbsZCdq3w7sZU7gzsIJbrO69cFj5bpdK8vZvl6N141qIBnFlVzRf/8hJUt0rb8YYfRTm63Ysli44OzZ8/uhLfR6s1GG83+cg4D2+aweKev9ueawQUMbreHskN1WBmEv1+yGLUl9AcnmmemIXEq05oK3zp/MrMM08GXQe1ywtYj0rpu3OB9N/q1ccXdPjUHE9PG5/bO87lO3vYfKNvhGzy8xkEg6NRM2HJAsWnZfDYtC32P75cuRrZ6Pxeqvo7dewMzCpV2yaVsk3Mf8fnnwb2PnfjzcU24ffYhBrbNgb61VBaEz/ikFLDY+PE/N50ZP6uv46xSePHDwODiM2fO5PKPDnJ69zzO7+NgC9oth/fW+ArHVlvH845s3FhDvVt5zl+5x3iGFy9ejDvMuxmMeJ6lbT9WU1VT571GKZ72ri+op7JPZNm2KmuBZQK9G/YbGIxdu7zRE/bvD/17pbLPWxX+kJAoY6Rs/Uh55l9SpOSMEBpFpBVwNtAdqAD+AwRaVAdBKfUs8CxAs2bNVGlpKWVlZZSWljoef9l0I6VX0YrA6aP8ulqghmZrm2LIrwYl5d5YbRXlFZROdr52QjHLeezIESGnKkLV1Z/m38xkV5Vh8zZu7IkB+3cv3ATzA1PFTTp3AKUjnEO5xMq6vHWwwvlr5qnP9KmB26x1YOLPE1qkiNsyd9VOWDAPBQwYOBAWfEPr1q0pLR2Z0PLsX7QFlnzL0OFHc8sXs3z25eUIf7zwJAC2z1sPy5cCMHzoEEfTAzvRPDMNgvk7O5Wp7ea9MHe2Z33kscfRLD8XPprG2EE9KS0NHTkg0rouYzWsMmzdJhzdm9LS2AIR91r4Oau3V9KhXRzPg+25H9i3N6Wju3u2jT0x8L2tdyuY4Zs7vHO7Ylz5VaHrbl5z6JDBHNvTq61uMe8zc6rXGyNwdK82PHPp0Y5pFsH5twvHnV9OZczA7vDaDxSVhI+HeVApIDA7FWVw+OU9YLmvMN2133D4aBbT1tXyj9/YAqKb9b7s1KN5zxZ656X/G8EYU2Mazzvybe1K1JpVjBkzBhGh6brdMG8OgwcN4rjekYdh6jz3M49Xdzzv6xeVy5izdYPnGlMum+Jp78o+lRStCN/2gDnlfhBZCaU3xl6eRPHK+m9gu+H4V1RURGlpcK162vV5vuSKyHzb+rOmXONBRHIwlGm9gKeUUvOSUpBkXDQJnASsU0rtABCRd4DRQLGI5Jraxi5A/EHIYiDVjRguhVs0hAuGHCzzQ6I8g+3E4gWYLljTgW6VXDsf6/dymp5efp/XUcY+vRhNTvB0obRPu6BhnfynTmvq3BTmGq2eyEfIbhcYTwaHji0LWb29ki/ChEiKFMvesKggl0qHlHf2Y+ys3l4ZsXOWf4ajglwX+w75zgQ8ddFQCvNyWPfg6by/aAsHa+o9ntPFTWMzF8nLcUVlrxvqSHswcjDyhf8YJo6nf73HJGiK3fo96t2G+YqKMciz9f5fNza2AYxFbo5QmwDv6XjiTWpCUqeUGh7qAKVUPTBYRIqBd0Wkv1JqaaILkilf5Q3AKBFpKoaP+jhgGTAT+Jl5zKXAe8kuiFOG0FRbSiTSpnHlNkPD/avjujvuP3uwowVAzF6goYgkxI7F0TEEtE0mTllMkhFeYc4aw2P64++3cWTHFj7x9Oy/Sb6P0Jgpr72XyZeP4B8XD3Pc52+vV13n9nijxpphwgl7e8Zz2VXbIpvqixSrjp/9fgwfXHdcmKPhr+cbcR7zclwRZ8xxdoQxhLBjzIDzViQCEeHswZ19Uo/+dGjobB3BqK5zM3ftbuqB6ghsVVe5gzvn2IWiYYe3YmCXYnZWVgc9vmvrJj71HnZ44voYj9Bo1skqWtRCo/kux9u35LokplSQ/ni/j6n+KhqkuyNMMlBKVWDIRhHPxkZDRnw9TDXr28BCYAlGuZ8FbgFuEpHVQBvg+UTcb/x+o+Nxh3jiBGhvvqipfi4TGXLHc80g2stgH5mkCI1RCDf2vLnpQEMJZqf0Mz7MR3VqgdutHMO6gK9gFcopIhPxN+CvqXN7PoA5CRTU7e0Wj6YxkTMD4C3LYS0K6d+5ZdDjRnTI4R8XD/UMDLfvr474OfXXTuea2XEAbjv9SMonTQgQXJrmewcwo6JMHWln0cYKnmuVw2+qHaad/VgWQmi0p8kryHXx/Za93PhmoKkNQLvmBRzXq61PvfMT+E5bv5nlsOzV0EX3XFnZeeJ9znPNuJHxOpE5KVVSid1ZsjELkCLSztQwIiJNgJOB5SFPipGMEBoBlFJ3K6X6KqX6K6UuUUpVK6XWKqVGKKV6KaXOU0oFHzZGQb75cDmFCbY/d2fn5gdsSwXJCKwdbdDhpAiNUQjD6aY9ayjBzPISFwytRbBnwT7N5j/llun4C8ovzF7H4k17gcQ+l76axtivW1MXf2gTO5EKDFcPLuT0AR19cijHqmm0ZxUK9jjZheNxUebV9ueHgsjK2VqCH/fthgrPckGuiz0HvdPr/k2491At+Tkun3on9lky/nvC7sSoabTe5XiLZs2MxBvgO92ExjsmHMUV5qxZBsbRjoaOwEwRWQx8A8xQSn2QjBs1rq9HgrCsb0IJjYLXljHVQmMyNI3RakNSPT196bGhUyk2NHatRB8znMJPhzpP7ceDJZzWuRVutwr68bBrTDLRpjEU3do05YXLvOY+L89dz8//ZXjKxiPc+ZOboOnpLXHmwwbfD2C0U/BjbQJcpM+Cf0D4vAiEKbvmMVYzgU4tCxkRRcarj+qCR1yYs9Yb/N5/UGzXQn2/ZS81dW4+Xb7dZ4CVSFMH67m0NOKxZIQB7zMZb9lycnzLEyup/hb60yQ/h7MHGwHPG7PQqJRarJQaopQaaCrW7g13jogcJyKXm8vtRMTZJs0PLTQ6kG/2IE42NF6hUVLuADO0WzGQWFu584YZtkfRahqTob2KRHv42q9Gcu6QzgzsUpzw+8eDveydiptQPmlCUHvQRNyntt5NvVJB09zZy5MMAT/VHNPD2eM0sdqh5AgQsdCuyJutKdqi9OvkTZ0ZuYY+cHrasxyiAB/dcALf3nlyVOWz06m4iWcK1mKH281t1QepcAjgXRGh2OI/KLYLz0s3G1rqTXsO+dQzkWMtq80sIc0doyOMVb54B0eWk1fcmsY0Sn5hYeUDLz0iPm13Y0JE7sYw77vV3JQHvBLJuamWe9KSPPPBdxqz2jWNqQ6R/MqvRrLvUPC4ibFgfWSj/dgmY3Y4kineY3u15dhekYeoaCiimVqP6z5WZ1+vqHcrhh3eis0Vh3j4ZwN9j7P9QOlm/5kIgj2viRTufHOhx37dUT1axx0M3/5tj/ZdtU9JRzpL4R/Sy/48hRJY4s1akpsj1NbZM6coPqmvZYtSfFVfx+m5fulBc/J4v95Z29ijXTPWmhmC/AfF9pkBu11hbtKmp/00jTEKW9bvEG//a5WnLgpPdSc838c0Ujl2bNmEubeO8wiPGgB+AgzB8BNBKbVFRCJ6WbWm0QGvTWPoJz83xeOppvm5AVlR4sX6oNRG0Hm8/mtvwPlk2FUm0vC8oYlmaj0eLMG6tt6N263Iz3VRPmkC5w/v6nPc0MOLGdv3MD6+8YRGqWkMpu0KpQWLFnsO5Hgu271tZDHvQmF30otWy2QXGsNpGhfeeTKf3DSG7n4pD5MlTPnjH3KnHqg0q77KIe1dqJIMMmcj3rhyVICjn30QUG27X6LsWP2xBjPW7+gRtmKdno5X02gzc0kE6dbDdGhZ2Cj7vTioMQOCKwARiTinaeZ+lZOIpWl08qrx0TQ2wmfQ0jyEShFmfUDsmsA2zRI/isvkOI0N5ZjjERrdinqlgjpFFOTm8MJlRyciXVVaEkyjmEjv6a6tvPlh4/lIXzWmB22a5QfkTo+GB2w5rKMWGqMIv9S6WT69DgsUcnMbyNzBSN3o7YtqgY7m/do7OL0Es2hUGFq9kjZNGdWjTUCb2ddW2/JMJ6ueOX42jbFPT1uOMHHaNNpmLOLB+qUa4aexsfGWiPwTI9b1r4FPgH9FcqKennbAo2kME3KnMTaepTG0a1X8sUal9qmtWIP3hiKTHTYaanraEgDW7qik3p16W7t0I5HtYc/1G48wenibZiyIw84PYHz/jvRp35wV2/ZHPTXp46ASYzXyG0hozMtx+UxP1wA5HpHEt39+oPoQKx3sHAFqxPBUtsr6zsJNvgfYqvDSnPUAHNmxRdI0qm/O3wjAN+W7DVvnGKen1+8yptvjLVqN2d9HMsMUCboXSl/MWNdvAn2BfUAf4C6l1IxIzs9cVU4SyTOFRacQvPZuqjEKjVag3sFdi4MeY2kn7LY/yZieTrcwOtHQUAKvpWl48cty3EolxbY0U/C344TE2tra7eDSwRPTmmKNR8sU62ykfYo7kdpcf75as5MVNs1fLcrz0fIf1gYTGMHQgNXXe4XGfVW+tuBONbh5fB9fR5gECo2F5rN0qMaohSdOY5RtacXcjHdw9MKX5QDMXh1flqLEBpPSJANzWnqaUmqGUuqPSqk/RCowghYaHak2X9x/1gZOUPs6wqTBlyPBHNurLd/ddTKlfYJ7mlmCSjTpvWIho4XGBoqFWFRgDF3OG9aFunp3o4vBGA3HO+TsTaQdml1QSuR1Y8XS9McnNMYmNdpnGZKR6cjCX7irUV4HxOBzIYEc8mgand8PpzrU1Suf9ymR/ZGV9q9bG8PkwfoZopX9Lh7ZDSBo1IRIufFkIz+73bM+HlL/dmjCsFBEjo7lxOz9woQgL8L3z+q8WjeyV6Q4TD7a35zQA4AebZt5hJZkkMmZSxpymrh5QS5Fhbm4VXoIM6miaV7gs5hI7ZA9RWM6yOaWEBtPHWONy5fvIzTGfPuwnD/cN/1gNcozw/NZfeSRI9bmG2nyLLnvtV+N9Nm/91CgNaRb+cY9TaTQWGDOzFj5uz1pBKP8lnz0/TbAiC0ZD1YO8niFz3QL7q0JykhgjoisEZHFIrLEDAweljTo+tKPTnXBXxz7S2G9GHlZ9oacPqAj5ZMm0KpZPl/dOjYug/5QNJS2LtPJzRFPyJ0MVs7GTVGhg9CYQImmqc2mMR2Ec2u6PBbB75bxfQH4cOnWmO6dH2HInXhp1TSfglyXx2Toe3d9TKHO2tRjvh9GuZ3CdPmHm+lc3MRHA5mfwEHs5opDAPxnvmFbqWJ0hDnpSGNGyB2n17MlEDfGkDsaR04FegJjgTOBM8z/YcniT0xwQunO7EKj9XplcyO2KMxLeNgfix2V3swZZw7qlJR7JJM7zziK968dnfT75OW4PMG9s9kRxknjlsj2sNvtJnNKNlIs7diOyuizpz40Pb60tLsPePNlJbMl8nONkDu1Znu/XVfLtjBT6ofbvKqvMOM4VuSYQqNDYc8Y2BGAar/0jv55vBOpabQSM5x8lJE73htyJ7rrDDJtzy0hNFYqqw2t7XuLtsR1Ha1pzAyUUuuBYgxB8Uyg2NwWlmyWd2LCm1geCs03o4ekOsx348QeHuahnw5IYUli44rjujdIpprt+6t545uNuN3BQ+5kK4lsDx9P2jRo58WbKgAo33kg6nMHhXB0i4R3vt3sWU5mU+TnuFAKim3Tph+Ywbu7iYvLqg5Q5pc6MA+4Lb+QB/ObYIlSM5u6qHf72ii+e/WxnHTkYTQ387ev3+WbKtGfRIYAswYgll24R9MYpbh1eGvDJvLIjvHZIm7bZwzQX5u3Ia7r2L+PmvRFRK4HXgUOM/9eEZHrIjlXC41hUEFGtQK0ERd35hdyaV5oG0BNbNi9VZvme/W/LZskPrxPY8AeUkRjkMj2SESomkRiaamGdGsV9bl/PX8QAC9eHpMtPJePLvEsJ1PranmIVzioCDea3tKT62p8tucAR7hy6OhycXSO0YeMOGSYb9gtXoZ0a8Vzlx7Nf83wO+8t2owTvc04lYmcnraExqpa03vasmmM8hbH9mrLnFvH8qez+sVVHntaynjQs9IZwxXASKXUXUqpu4BRwK8jOVELjUEY5TJHgn7b/V+Knq4c8tNA69AYyQ8S63DWzSc2cEnSn/xcQyOTDrZ26USypuvTYXr6T2f15xejujG2b/Q5dXu2K6J80gRODBElIRQjSlp7lpPZFKFsLoMJKD7OK6bOq16CD6q6tDLSalZWOTvWrNpuhLVJ5PR0oV8SBasusby/HVs2ift5tAYgEF+sRk894iqNpgEQfAMQ1BOhglj/tkGwpjWm+k19aJuN1KM1jb4M6tLS8xHXmkZfEplG0E46BJ5v17yA+88ZEHRwlUx8vKeTeJ9vN1REdJx9RsjeGlZPUStGijynkDuPmPE9R3RvHbDPTiID9ltT3Us3G17PsWaESXR5AHbGYCNroTWNGcOLwDwRuUdE7gHmAs9HcqIWGoOwyG0I4e/XB0tMpdGkBwV5ORyoMbQk2S40rrz/NH64d7xnfW0M9n6RkO3t3FAxKwd2aRn+IODHIGZEltBYh+EZ7JQTvn0Lw5HPmir2p5WZ7SoZP/nHy4yQOZ7p6cTfImpufjuiyCuOaKVKZqCU+itwObDb/LtcKfX3SM7NGKFRRIpF5G0RWS4iP4jIMSLSWkRmiMgq83/0xj1BaBPksdcvhSbdaJqf45lay/bp6fxcl0+6vx37Y9eahCKbg6iDb8idZD5ydlu9e/KDR2k4ZNNx2Z1JrPdhT45wqLaewvxAp0XLXvpgjbPQaO1/umxNFCWPjli9pxNJ+xaGXeMXq2LPCqND7mQGIjIKWKWUelwp9TiwRkRGhjsPMkhoBB4Dpiul+gKDgB+AicCnSqnewKfmekJ4oMCwc+kqvk2khUZNutE0P8cTMiOb4zQ60bYoOU5qmRx4PhEU2MMPJbE3HNKtFeWTJvD3rbUUhriP3RpxizvQJm9eUxdrdxxgd6W/lbo3/uZny7c7XjvWrDnR4I3TmLrn6qGfBqbhjBb9fcwYnsY3U3KluS0sGfGJEZGWwAmYc+5KqRqlVAVwNvCSedhLwDmJumeB+fJuDJHPVKNJB5SCH/caITOyXdPoz2n9Oyb0em1NL9Nk2UpmCvZMUA31yHUIod21JzLZ7WBZl28Gv56zdlfAPislYjANmxWv8e4z4/NQDkU6TE+POaKdZ7k6xoJooTFjEGUzBFZKuQkdotpD8nLAJZbuwA7gRREZBCwArgfaK6V+NI/ZCrR3OllErgSuBMjNzaWsrIzKykrKysqc71YKlQWVsMh49Cv7eAXy6m3AVuFAnwOOgWIBKCH4tVNAyLqmOfePbkJlrfIpfyrrko5t+eFSr93eujVrKKuPL9YapGc9o6FLkbCpUvH13K9oFiZlUzR1tZwEFiz8lsryzIrPmsjfdE+VdzD9xRezHG0FE0qp0Sf/3Q03LAm8174uh2Cdd7u9zz5iDRyqU2ysMvaHaoMpH33mWbaO27PLGJCtXP4DZXtXee+RoPZ8/+OZLNtpTI1//fXXbGiWel3OlhFCs2KjDesL6n3aMxQHK4E1ggxKr29gJGR6nxcla0Xkd3i1i1cDayM5MVOExlxgKHCdUmqeiDyG31S0UkqJOFtSKKWeBZ4FaNasmSotLaWsrIzS0lLHm025bApFJUWA8TEuWlHk2ZdfVwPU0nxFs6BanYryCkonO187FYSqaybxTo897NhfTWm/DikrQ1q25fSpnsU+fXpTekxJ3JdMy3pGwftH1/Ddxj2M7es4jvQhqrqabT1w0GCO6dkmjhI2PIn8TXcfqIGyGQCMOWFM0j24vX0yWP1yexGuzivg7poqXJsKAa/9qr3Prq0+5DNj5NgG5u96w8xDAcetcq1l/rQfGHfsUIYd7vWwjrs9zXvub9mDvm1zYPEijhk1ksPbNIv9mvFilumx9blM3maUo7JPpU97hqLQXQ9UIYug9JrSJBUyOWR6nxclVwGPA3dgKIg/xVSshSNThMZNwCal1Dxz/W0MoXGbiHRUSv0oIh0BZ6OUOKlQbopN20Z3GkwjZCtDYwhinG3o6WmD1s3yIxIYYyXbm9kn5E4Dt8VQVw59XTmckpvHdtN+cW6916rxdj+HmXU2gdFy9oiGy0eXMKpHGwZE6MkdLbe/u5S/nGcEW0+mfWhDoP1fMgOl1HbgwljOTb0ePAKUUluBjSLSx9w0DlgGvA9cam67FHgvGfefVx/oVZcOwX01GoDLji3xLGd7KJhk89ENJzC+XweGH57dAxi793RDD1R+l1/IKblGGJx889YL3d4+urcruNnAg+dGn440N8eVFIHxUVNQBLsjTMJvExUvXhZbhiCLdLDN1IRHRB4WkRYikicin4rIDhH5RSTnZoTQaHId8KqILAYGAw8Ak4CTRWQVcJK5njBOzzE6Jvu4VY+kNOnG0bbsHFpoTC59OjTnmUuGJTQPcSZiD26eyieuIMq7r94emW3eyvtPi6U4UXHGIK+TVjqE3AEo7eN1hjkQg9e4DrmTMZyilNoHnAGUA72AP0ZyYqZMT6OU+g4Y7rBrXLLu2dF8g1+sq2GMObJV6FGUJr2wd/Rut+6tNcnHPtOSSkHHPzeUUyzHUa4c5pqayNbNwk9PTxjYsUGy7Ni1tekQcsf//s/VVnN9iNiYIa+TqAJpkoUl+00A/qOU2hvps5fdw+UwHEx1ATSaCGhmC3/y1vyNKSyJJhtJpaCT63fvzhL4STvKNl3906GdHa9z1qBOnuUmeQ3jFW9vt//M32Rsa5A7R8Zah3iX4bDOSKd6ZAMi0lVEZorIMhH5XkSuD3PKByKyHBgGfCoi7YCqSO6lhcYQnJITqIjVmkZNOrMwwly9Gk1jJM9BgC3w0Yo6996P/3yIZzkVMTjnr98DpH56GuC0/kZ0ir0xGWOZGtMElkcTEXXA75VSRwGjgGtE5KhgByulJgLHAsOVUrUYOrKzrf0icnKwc7XQGIJgHYx+ITTpSvOCjLE40WgahGj9pd/4JnXa+nSIfmAPYq6itGvUqTBSg1LqR6XUQnN5P0bGPGe1uvec3UqpenP5gOlwbPFQsPO00Bgl2mJMk45Y2oGFdwUdIGo0WUm0zjINyTUn9vRZT4eSdmjptWPcFmMKxXSoRyMjV0Tm2/6CxlQUkRJgCDAv2DEREPQn1GqJCLFiNerpaU068vQvhlHvVtp7WtNgdG/bjHU7D4Q/MMUURPlKXDyyW3IK4sAfTunDUzPXeDek2et7f82hqEKSaJvGpFGnlHJyBPZBRIqA/wI3mN7RsRJ0tKCFxgi5ofoQkwtTGKlfowmDFhg1Dcl/f3ss63ZGFsImmdyf34QF7joGB4nPGK2mcXhJw8Xg9DeBSofpaTux/ro65E7DIyJ5GALjq0qpd5J1Hy00hqEFYInrNUrhRs/pazQaTetm+bRu1jr8gUmmi8tFF1d+0P3RahpzXKnr4dNFZPzZ3nrebhm9F7knTqM25GpQxBh9PA/8oJT6awIuWR5shxYaw3B6bj5v1NUAcGX1QcY7eFRrNBqNJj3Jj1IU274vosgjSSHVcRotRh1ye4TGaMwavUKjpoEZDVwCLBGR78xttymlptkPEpFzQ13E0lAqpYIepyWgMJyck+sRGgE2K6U1jRqNRpMhtBDhN7vruObx00Me5xJwK6ipb1gf4ItGduO1eRuA9BG27ILBhkPQL+iRvmj9YmpQSs0mssfnzFCXAcJOa2uhMQw5fiO/Je56YouRr9FoNJpUcGSNokWhf/4YX34x6nD+PWc9TRsouLfFn87q5xEa082mEeCF9fBohJoSS2jUipX0RCl1ebzX0L9tBLxY0NRnPf1ea41Go9HEw5EdWwDQrU3TMEcmFp9g4mn0cbk417AT3VUTeaG0pjEzEJH2IvK8iHxorh8lIldEcq7WNEZAutiZaDQajSY5XHh0V/p1asHALsUNet90yePtT0kMDkHapjFjmAy8CNxurq8E3sRwpgmJ1jRGyAs2baNuNI1Go2lciEiDC4z+pNP0dC9bHu8KFZmdp0do1CrHdKetUuotzNCaSqk6oD6SE7X8EyEuEUYFiQOm0Wg0Gk1jwq4BnVpXG9E5WtOYMRwQkTaYP5mIjAL2RnKinp6OghzzVUj/HAgajUajyRSm/e54pi35kWb56amYmFFfx8V54bN4awVjxnAT8D7QU0S+BNoBP4vkRC00RoFOuKHRaDSaRHNUpxYc1alFqosRN9p7OjNQSi0UkTFAHwzF8AqlVETq5Iz6bUUkR0S+FZEPzPXuIjJPRFaLyJsiEjwtQALIqMbSaDQajSYOHspv4ll2RxDlW2ldY0YgIoXA74D7gD8B15jbwpJpctD1wA+29YeAvymlegF7gIhcxmPFaiwdp1Gj0Wg0jZ32Ng/qrVGkhtGTcmnPvzFitj8BPGkuvxzJiRkjNIpIF2AC8Jy5LsBY4G3zkJeAc5JZBquxDpeMaTaNRqPRaOLmtppDYY9xm3KlFhrTnv5KqSuUUjPNv18TYeIfUdEklkwhIvI28CDQHPgDcBkw19QyIiJdgQ+VUv0dzr0SuBIgNzd32IwZM6isrKSoqMjxXhXlFeQWBJp7vr0ZqhcsIadjOy48ukPQstZV11FcUhxdBZNIqLpqoiNb2jJb6gnZU9dMrmewPjlSktEnZ3J7hsPe3rctg321hhj45KDQ8sKc3fDqRuGunrX06F2c7GImlHT+PU888cSDSqlmibqeiLwCPKmUmmuujwSuUUr9Mty5GeEIIyJnANuVUgtEpDTa85VSzwLPAjRr1kyVlpZSVlZGaanzpaZcNoWiksCHp6C2mslv3AZA2d0zg96voryC0slRFzNphKqrJjqypS2zpZ6QPXXN5HoG65MjJRl9cia3Zzjs7f3TjpW8aGQ5pGhF6N8gv64WqEHmQOmvS5NbyATTmH9PCxFZguGvlAd8JSIbzPXDgeWRXCMjhEZgNHCWiJyOYVLYAngMKBaRXDMwZRdgczIL4dJKd41Go9FkEb1tcmK1UhSECECu4zSmPWfEe4GMMM5TSt2qlOqilCoBLgQ+U0pdDMzEG1voUuC9ZJYjIxpLo9FoNJoE0SLPuzzfXRfROVpoTE+UUuvtf8AhDFnf+gtLpstBtwA3ichqoA0R5E2Mh0xvLI1Go9FoYuVftTU+6yvd9VxedYA60zcismSDmlQjImeJyCpgHfA5UA58GMm5GScHKaXKlFJnmMtrlVIjlFK9lFLnKaWqk3nvjGssjUaj0WiSxAM1VSjgV9UHfbbrb2Xacx8wCliplOoOjAPmRnKi/m2jIEePozQajUaTZVyQG5g3o9xd77PuVkp/ITOHWqXULsAlIi6l1ExgeCQnaqExCnLrI7Pn0Gg0Go2msXBqjtdnttqcir6npsrnmKts2kbJjEh+2UyFiBQBs4BXReQx4EAkJ2qhMQry/EZWGo1Go9E0dlw2j+lKFM/4CYwANXhtGrUjTNpzNoYTzI3AdGANcGYkJ2ZKyJ20IK9eC40ajUajyV5+Xx08M8xrdYajjBYa0xullF2r+FI052pNYxTkRhhuQKPRaDSabODpgqapLoImQkRkv4jsc/jbLyL7IrmGFhqjQGsaNRqNRpONPOEgHF6Sm08Th2Df+dqmMS1RSjVXSrVw+GuulGoRyTW00BgFOUoLjRqNRqPJPpo7CIfjco3I38/4CZTa7q3xooXGKMjT3tMajUaj0XBzXqFnuVCEY1w5AHQIkWZQkxxE5AUR2S4iS5N9Ly00RkGu9p7WaDQajYb2Ll/h8Df5hUwubMYkbeOYCiYD4xviRlpojAIdp1Gj0Wg02cpt+V7tYmvtI502KKVmAbsb4l7a9CAKcpUhNNaJlrU1Go1Gk10cbvv2iZ6GbkhyRWS+bf1ZpdSzKSlIKm6aqeSa3tNulxYaNRqNRpNdFGhBMVXUKaUiSvOXbLTQGAW5biPefb1p8KvRaDQaTTbxcH4T8rXsmLVooTEKcszg3lpo1Gg0Gk02cpieactq9K8fBVbInXrRQqNGo9FoNJrUIyKvA3OAPiKySUSuSNa9tKYxCnLMkDt1WtOo0Wg0Go0mDVBK/byh7pURmkYR6SoiM0VkmYh8LyLXm9tbi8gMEVll/m+VzHLkmdPT2hFGo9FoNBpNtpEp0k8d8Hul1FHAKOAaETkKmAh8qpTqDXxqricNy3u6Tk9PazQajSYL6fnx03Sb9Uqqi6FJERkhNCqlflRKLTSX9wM/AJ2Bs4GXzMNeAs5JZjlytKZRo9FoNFlM1zlv0WPm86BUqouiSQGiMuyHF5ESYBbQH9iglCo2twuwx1r3O+dK4EqA3NzcYTNmzKCyspKioiLHe1SUV5BbEGjumf/xJ5wy+THWte7M908+E7SMddV1FJcEFCNlhKqrJjqypS2zpZ6QPXXN5HoG65MjJRl9cia3Zzjs7V1fUE9OtTG71n3aFPq98jwA0597k7qmzikD0+0bGAnp/HueeOKJB5VSzVJdDsgwRxgRKQL+C9yglNpnj0ivlFIi4igBm5HTnwVo1qyZKi0tpaysjNLSUsf7TLlsCkUlgQ9P7g7jfnWuHIpWBH+4KsorKJ3sfO1UEKqumujIlrbMlnpC9tQ1k+sZrE+OlGT0yZncnuGwt3dln0rP984SGAE6zSxnd68RnvXSP53oPf/Sd9PqGxgJjfn3TCQZM88qInkYAuOrSql3zM3bRKSjub8jsD2ZZbByT9e6MkrW1mg0Go0mLvIO7vVZH/jqLRSXfwfACfed7LNP6msbqliaBiYjpB9z6vl54Ael1F9tu94HLgUmmf/fS2Y5ckyhsS5HO8JoNBqNJnsY/vT/BWwb/NKNHGzdGZdp72+RW3uooYqlaWAyRdM4GrgEGCsi35l/p2MIiyeLyCrgJHM9aeTWGaMnrWnUaDQaTTZRULkbgMUXP8T643/h2d5092bP8qrx1wLgUvUNWzhNg5ER0o9SajYQLNvluIYqh0fTqIN7azQajSZLaL55uWd5d8+j2d1rBLWFzek142nP9q9ufIs2q78GwOXWQmNjJSOExnQhx7TTyCx/c41Go9FoYmfYc78FYFv/sWA6oG469nxKZv2b3OoDLLziKWpatEOJMXkpyh3X/Wpra9m0aRNVVVXxFTwKWrZsyQ8//NBg93OisLCQLl26kJeXl9JyhEILjVFgxWkMpvLUaDQajaaxsnr8dT7rsyd+4LOurBjGcWoaN23aRPPmzSkpKcEeJSWZ7N+/n+bNmzfIvZxQSrFr1y42bdpE9+7dU1aOcGSKTWNaYGkadVBTjUaj0WQD/V/0TkHXNisOeWyiNI1VVVW0adOmwQTGdEBEaNOmTYNqV2NBC41RYNk0Dv5xRYpLotFoNBpN8imZMQ2AtWOvCH+wae8fr9AIZJXAaJEJddZCYxRYmsaC+rowR2o0Go1GEyHV1bB6dapLEUBr07EFYMNxF4c9PlGaRk36om0aI0UpDv9ueqpLodFoNJrGRmGh8b+iAlq2TGlR7Ax89RYA1o++yOMAEwrLpjHbhMaioiIqKytTXYwGQWsaI6T03rGe5XrRzabRaDSNip074corYf/+1JWhpiZ19/aj6c4NnuV1434V0Tla09j40dJPGKSuxienJkCOcoN+KTQajSb9UYqRnz0IX3wR+rj77oN//QveeKNhymVRVuZdrk+f+IYjnroUgA1jTopIywgk1KYxlUycOJGnnnrKs37PPfdw//33M27cOIYOHcqAAQN4773ABHRlZWWcccYZnvVrr72WyZMnA7BgwQLGjBnDsGHDOPXUU/nxxx+TXo9koKenwzDmz6d6lteOvoh3XC7+8MUriNuNytEyt0ajyVIOHICtW6Fnz5QWo9m2tRRtW8Perv2oatUpYH/h3m103Pg1nHBC6MgX69YZ/3Mb+LN4ok0p4U4PYavHD95QOouv/B1FKyM7Lxmaxj/973uWbdmXsOsBHNWpBXef2S/o/gsuuIAbbriBa665BoC33nqLjz76iN/97ne0aNGCnTt3MmrUKM4666yInFdqa2u57rrreO+992jXrh1vvvkmt99+Oy+88ELC6tRQaKExQrYMO5PV435F/ecvAcZLoQPvaNKGc8+Fd9+FDRuga9dUl0aTDYweDYsWGdoxV+oG0ANeu5XCfdsBKLt7ZsD+gn07IrvQ7NnG/+3bE1W08Mya5bueDppGpRj49fMA7Oo1MnItI43HpnHIkCFs376dLVu2sGPHDlq1akWHDh248cYbmTVrFi6Xi82bN7Nt2zY6dOgQ9norVqxg6dKlnHzyyQDU19fTsWPHZFcjKWihMQT5+3d5lleecRM5SnnsGUWnSdKkE+++a/x/8EH4xz9SWxZNdrBokfG/thYKClJWDEtgBMg9tJ+6Jr4Bmu22eRw6BE2aBF7kL3+BPXuM5dmz4ZZbklHUQMaM8V1PB02jbQCw5OJJQOQOHh5NYwLrEUojmEzOO+883n77bbZu3coFF1zAq6++yo4dO1iwYAF5eXmUlJQExFTMzc3Fbau7tV8pRb9+/ZgzZ06D1iEZ6PnVEPT45J8A/Dh4PGA0Vr1ls6GFxoZHB1V3ZqZNu9K/f+rKkU643YYnaqJ57z1D85KMa2cStbXOyw1A61XzKC7/jqY71uOqOeSz77iHzyLfT7PY539/8a7MDNREAvDHP3qXP/jAV7tWXW0MyhIt0L31lnfZtHujLsXh3JYs8Swu/L8noj+/kdg0gjFF/cYbb/D2229z3nnnsXfvXg477DDy8vKYOXMm69evDzjn8MMPZ9myZVRXV1NRUcGnn34KQJ8+fdixY4dHaKytreX7779v0PokCi00BkMpOiyeAcCqCTcCRuDNOvOliDdNkiZCXn4Zfvc7Y/mnP4UUpnlKW+64w7ucRt6XKeWqq6BVK9i9O7HXPecc4/9XXyX2upnGpk3e5erq5N6rvBwxU7i2/246A1+byOCXbmTEPy7jhAdPB2DFGb/3HH7s384Pfq2773be3qtX8HOeftow/5g2Leqih+SCC4z/jz7qDbmTyvf30CEYONCzuq9r9APQxuQ93a9fP/bv30/nzp3p2LEjF198MfPnz2fAgAH8+9//pm/fvgHndO3alfPPP5/+/ftz/vnnM2TIEADy8/N5++23ueWWWxg0aBCDBw/mqwztQ/T0dBA6f/2uZ9mdm+9Z9tc0umqrjf0ZEMk9I/nlL43/jz/unYLduFHb7dlp0cJoj40bk/8BT3f838NFi3wdDeIlP9/4sB84kLhrxkp1NUyfDmedFVv/8+23MHhwbOdaTiNgCBvJ4osv4IQTODvMYZUdgjjjmP30wWZtaXpgJ8yfH3hMebkRWPu++4wB2HnngV0L9Pnnxv9Zs8DmGZswbrrJ0GADxJJCbvFi6NvXeDbjoWlTz+KUS9+lOIZLNBabRoslNs1r27Ztg04v22M0Pvzwwzz88MMBxwwePJhZ/jasGYjWNAah93RDNf/VTW/7bLfbNOYe3MsJD4yn/5t3Nnj5UkpVlXeaZ/5848OVDOxG4Tff7F3u1i0590s0f/tboKF7MigvB3NEm9VCo9PUnmWnlgiU8mqCUhnLz+KRRwzNpz1kS6Q89xwMHQpHHBHbvV980bucTKHRspsMwb5Ofdjf+Uifbcc/cBr9X7+d0vtOAuD7ob+E8eNh+HDjgJoaQ1gWgR49jG0jRhj/q6vhhx+8z9OUKcb/LVvirY2XvXuN/6Z3rkfTGK3QOGUKDBoEt98eX3kWLPAux6HtbEyaxkxDRMaLyAoRWS0iE5N1Hy00OlC0dzMA+zr3paZ5G599dbaRVLMd5QC0XfFl8IvZ7fDq6owX0sk2r7ra2B9NVPnqati1K/xx0bB8udGJLl0a/JhrroGxY+GBB+Doo+G006L/cFidtsOIzMOaNd7lRx5xPqauLvFTkImgutrQIFiG7skycHe7jd+sZ0+jPaur4ZNPnJ+jt9+GhQuTU45ks2KFs5bIzvHHB27bF0GoDqVgZQQxRVbYcs7Ho2msqDAGPvHYNK1dC3eag9VYBONf/9r4v3Nn6OM2b4bXXw/cbhduQr378XoDf/sttG1LbZ6hBdvWfyxbhhravuqiNsz+4xQW/urpgNNyaqtou9I7/bej00DIyzOeoU2bjCDeFlZ/bHq28r//Gf8ffNB3IBLJsxQpVsxIa4ozVqHR0lA62NdFxcaNxv+PPjLaKVbSzaaxqsr4Tt16q69JRaSkgzd7BIhIDvAUcBpwFPBzETkqKfdSGe5cICLjgceAHOA5pdSkUMcPF1FhPj0e5l33Codad/bZ9u0n/+LGL1+LqaxJo1072OEXVqJpUzh40LveoYPR6RUVRRZSolWrxGppUkFenlFvq0N04vDDvR3u6NHwpTkAOOMMY9lqA//2TAQicOSR0KcPrFrlK6h37mx8sO2cdJIhEJaWBmqXHnnE15jfn6OOgmXL4ivvLbfAQw9Fd063bkYYoGziyCMNTdWECTB1amzXuOgieC2KfuZnPzPuddZZhoNFsH69bVsjXuE77zjv79vXGIQEo6AgMm22yxU4UIr1GSwtpWLxBop3r2XWbR9SsG8nI566lEW/fJSKksGew5ruWE+nb96jyzfvBlxiyqXvcs7S+301av5YbXbffXDXXaHL1LKlV1to54wzjAFFMIcbf7ZsgY4dYe5cOOaY0Mf27Ok7kI6UoqLIlRF79kBxMVMum0JxSTEAlX0qKVpRFNHpzTcvZ9hzv/VuuPpqY7BlOoRERLjnZOBAY0o+2fToYQzOEkWk744DAgeVUs2C7hc5BrhHKXWquX4rgFLqwZhuGKosmSw0mtL1SuBkYBPwDfBzpVTQJy4aodEp5lfe+sWMnnx9DKXNMESS763cqVNip3wSSZMmyZ12SzQ//mhofWMZTUeKkyCQjixcaAjd7dsn5/odOxrtnam0bm1o5487zhubMJ15/XWm//cAhzU/yL5uA4xtSjnaYrpqqxn9yDkcatWJ/AN7EHc9a8f9mh/aHMc51x9uTMlbXHopHHaYMeA64wyvhhGge3fD7MPikksMp7xE43Yb9dizx/hdUsmxx3oGzbEKjXkHKhj9l594N7RokVgNLUBOTsZoABOFQA2wxLbpWaXUs579Ij8DxiulfmWuXwKMVEpdm/CyZLjQGLV03axZM3XgwAHKysooLS11PMb+wkSM1YkpRcX6vZwz+Zzozo8F2z19hDy/5bKZM711tR/vfy1ru/+1rP0WTuf6C5n282IxtLef57Tsfy//utvv6f+MO10rVBntbRniuQlbj2DbImmjcL+10/mR1C9IO5aVlVFqOZBE+hsG60uirWs0RNIOYQj7m0bSfvbfxP/4YO9QLM9BsHs6PRt2rGd3zJjg775/gG57n2CvR7iyBXsW/cvsV75Q58TUJ9uoKK/w9snRPidB+p+yzz/3fW4sATBVTpGh+ugoiVVotMoR8TcwSB9VW1vLpk2bAuIgJpOqqioKLTOBFFFYWEiXLl3I8zMREJFwmkYtNEZCpA0lIlcCVwLk5uYOmzFjBpWVlRQVOb8IFeUV5BbE7lheV10XVweXaELVVRMd2dKW2VJPyJ66ZnI907FPzuT2DIe9vesL6smpzonq/HT7BkZCOv+eJ554YtpMT2dFyB1TjfssGJrG0tLSsJrGopLYH56K8gpKJztfOxVErR3TBCVb2jJb6gnZU9dMrmc69smZ3J7hsLd31JpG0u8bGAkZ/nt+A/QWke7AZuBC4KJk3CjThcbNgD1gXxdzm0aj0Wg0Gk2jRylVJyLXAh9hOAW/oJRKSsqZTBcaG0y61mg0Go1Go0lHlFLTgASnLQoko20aAUTkdODveKXrP4c53g0cwhCYU5zos8HIpromm2xpy2ypJ2RPXbOlng1FtrSnrmfqaaKUSou42hkvNMaKiMxXSg1PdTkagmyqa7LJlrbMlnpC9tQ1W+rZUGRLe+p6auykheSq0Wg0Go1Go0lvtNCo0Wg0Go1GowlLNguNz4Y/pNGQTXVNNtnSltlST8ieumZLPRuKbGlPXU+Nh6y1adRoNBqNRqPRRE42axo1Go1Go9FoNBGihUaNRqPRaDQaTVi00KjRaDQajUajCUujFhpF5FwRaZXqciQbEWkuImIuS6rLo8kMRKS1iLjM5Ub73IjIWBFplupyNAQikm9bbrS/qSax6L5AEymNUmgUkV+IyFzgOKAq1eVJFmY9FwKPA38FUNqzKWZE5EoRuU9EmqS6LMlERC4Ske+AvwEPQeN8bkTkYhFZAJwI1Ka6PMlERC4RkTnA30XkRmicv2lDkC39AOi+QBM9mZ572gdzhHQZ8BxwrFJqXmpLlHjMOuYBvwV+ClwLbAA+FZFZSql3RUQa44ufDMz2zAV+BdyCMcj4GPgileVKFiIyDrgGuA7YCvxLRHorpValtmSJQ0RygRuA24HTlFJzU1ui5GA+uwXARIyP4R8x+oY/icgipdRnqSxfJpFt/QDovkATG41K02gKSt8ArwPVIuISkUtF5MgUFy0hiEiBMqgBlgI/U0p9pZTahBFjqg80zpFiMhCRfLM9a4GFwJHAP4HLRaRNakuXOOxTlsAg4AOl1BcYAscmYHtKCpYklFJ1wCrgFWC9iOSLyE9FpFOKi5YwRCTPfHargCXAT5RSs4HZwJdA+5QWMIPIln4AdF/QGPuChibjhUYR+ZOITLBtWg18BHwALAKOAV4QkQfN4zOyziJyK/COiFwvIkcopT4FdtjqMwzYkroSZhYicjfwmohcJiKtlVLzlFKHgKeBLsBJmfqs2LHV83KzPvOB8SLyGjANaAW8KiJ3mcdnZJ1F5DYRGWnbNAdYD3yIIQj8BHhJRG43j8/IeoKnL3je/E2bA+8Ae0TEZQo+A4H9KS1khpAt/QDovoBG2BekBKVURv4BrTG0a3swhMM8274uwB1AT3O9F/Aj0CnV5Y6hnt2BzzBGSicCTwGPAM3N/S7z/+vAQL9zJdXlT8c/4EaMqadxwMvAY0BH2/6fA+8BPVJd1gTW8xUM29eWQD7wBHCKeVxfYC/QNtVljqGOHYH/AhXAKr99xwAPAF3M9f5mf9Em1eWOsa59ga/M3/JMjIHxNUC+7Zgm5rPbJdXlTfe/bOkHHOqq+4IM7wtS+ZfJEvYBYIpSqhWwGbjJtm8L8JBSag2AUmo1Rmd7eIOXMn52Y0wh/EIpNRN4H+gE1Jq2i25zyqGLUmqxiAwWkatBT1M7ISI5wBDgT8rQ1t4HHMSwewFAKfU6sA8YIyJHi8jFqShrPDjU817gEHCrMswbumAMtlBKLQemAp1TVNx42Av8RylVDFSIiL0f+Aaj/psAlFJLgelA2wYvZWLYD7xl9gX/w9AwHqOUqrFpS1oARUqpTSIySEQuSllp05hs6QdA9wUmja0vSBkZITSaRso+KKWqgVnm6t3Ar0Wko7nPrYxpGkSkiYj8HUMzuaxhShwb/vU0hcK9wL9sm78HumJoVi2h8GigmYhMAp4nQ37XhsZsz3pgG4bBOxjmDO8AR4rIMNvh/wb+Ye4rbNCCxkmIer4N9Dffkx8xzDb6mO9HR2BdKsobD0qpgxgfOTC0Kbfb7LbcZj+BiOSJyBMYQtX6hi9p/CilNuPbF8wDWpq2zm5z2zCgUETuAV7AcIzJaoL0q42yH4iirhndFwSRCbKmL0glmSJceB4Q+8OilKo0X4pvgM8xRovYji0FPjVXJ5gCWDrjU09LKFRK2e2TRgIb/bZ1wpiCBzheKfVk0kuaIdjtVWxC9rNAFxEZZn5sy4GvgcHmOb0wnqVXgD5KqecbssyxEEU9vwGOx+hUf8CYkgPj/djXcCWODSf7I6XUfvN9mY3RDzxjbneb55yNYddUD5ynDOeRtCdIXQ/YVsdi9AXVtm1HYjg4FGD0BS8lt5Tpj/+MS2PuB6Ksa8b2BcFm0RprX5BOSDrPYIrI6cBvgDXA+0qpMnO7C4wHQURylVJ1ItIOIzzCmRgq5wMYL0Zzc4SetkRZz5uAXKXUwyIyGmOk1ASoV0qtTU0N0gsROQvopZT6q+kcYHUYLtt0/g3AMKXUBea+x4FFSqnnRaQ1hp3Y1lTVIRLiqOdSpdSz5gCsqZ8gknaEqKeA8QGxvR/tMT6AR2B4EO8H3BjvTHlqahA5Udb178BcpdQbIjIcWI5hk7ZPKbUyVXVIF8RwkLwYWAm8YpopISI5Sqn6xtIPQFx1zbS+IFg9XRivR6PpC9KVtNQ0murjR4F7MEYKFcDPRWQEeKaf3aZqPd/ctgPDa3oFhudbgVJqXzoLjFHW05oaKQFaiMiz5nktlFKrtMBoxOQSkVswjLz/IiKDzfbLAe9oE8MA/GWgjYjcLiI9McIV1ZnH7U7nD0UC6lllHqfS+SMRQT2tj0Q7zJizSqltGFOJ24HJmPZ96f6RiLKuBeZpzYB2IvIihp1aW6XU/GwXGEWkUESeAe7CcBDsDlwlIt0BlDFdCxneD0BC6popfUG4erobS1+Q9qg08MZx+sPQvFnez52BNzFGSWA8FA9j2DQejTGtewaGHcbNqS57kuo5DGiO4eSzArg+1WVPxz+McAqFGKPquX77cjA8Bd8DOgD9gD9jhJ64K9Vl1/WMqZ6PA1OAozAGwZdgzDD8MdVlT2Jde2NoTg5hxGu9IdVlT7c/4Hqgq7ncFyMCRUdzPa+xvB/ZVNcI6tlo+oJ0/kt5AWwPxM+Akbb1fPOHzzfXpwGnmst9MNLmtbId3xtomep6NEA9f4sOE2Bvz98Bk4DzzXV76KV1wEW29UHAi/b2NLcXpLoeup6JqScwHChOdT0aqK43Aq1TXY90+LO15XnmehMMZUKBuT4DGGouD87U9yOb6hpvPTOpL8ikv9QXAA7DMFjdgjFKsOIOumzHtMJwaOngcH5uquvQQPXMb4hyZsqf2XnciJH94mcYtiuXAYfZjvkJsDnI+TmproOuZ0LrmRH9QDb9piluy3a2Y7qa+1tkcltmS10TUM+M6Qsy8S/lNo1Kqe0YqvPxGK7/v7F22Q7rBuxVSm0VkS5i5My0PIzrGrTAMZKAetY0aIHTHGX0DicCdyil3sboZAYCp9qOeRdYKSJ/ABCRk83/LuW19UlrdD0jrmdG9AOQPb9pQxCiLcfbDhsArFBK7RORTiIyBDKvLbOlrgmoZ8b0BZlISoVG8YaUeAIjhuLHwAQR6aiU4QVl7u8C5IjIdRhxmDpA5gSvzpZ6NhS29pyPETICpdR0jByj/USkj+3w3wIPi8hWjNBEKK+jSFqj69m46gnZVddkE0Fb9jP3twOqzH71I4x+NqPaMlvqmi31zGQaVGi0PAEtrB9YKVVrjg6+wggb8TtzuzViOBkjlE4v4HSl1KsNVugYyJZ6NhQi0tL87+8dvBpoLiIDzPXPMbwEm5vHD8YIhvxfDNuXtI5Zp+vZuOoJ2VXXZBNDWzYx188BrsLoV8crI5tOWpMtdc2WejYmGkRoFJHhIvIycJfp6m9tFxGfyO47MdLk9TGnZ9ub29/AyI15vUrvEDpZUc+GQERcItJCRD7A8IrDml6xCeVfY4THOEWM2FzLMDzQh5v7dwFXK6XOU0ptadgaRIauZ+OqJ2RXXZNNnG05wtz/MjAu3fvVbKlrttSzsZJUodF8OJ4E/onh4NERuEeM1H4uZSIiBWKkwqpXSs3CSJW3FCgTkd5KqblKqU+SWdZ4yJZ6NiTmiHM/hnd5ZxGxAtLmWh2MMgK7zgd6AhPNU6sxU0MppTYqpZY0dNmjQdezcdUTsquuySbOtlxr7n9HKTWzocseLdlS12ypZ2MlqUKj+XDMxBgRTAYewXD8qLfU0CLyJ+A5DEELEbkKw0nkn8BApdSqZJYxEWRLPVNAX2AHRnqri0WkuTWVLyL3icjzwAKM0eoIEVkA7MawcckkdD0bVz0hu+qabGJty49TVeA4yJa6Zks9Gx254Q+JDhEZBexWZlYCpdR/ze0nYSRJ/wZ4QERewEj31xsjyGi5eYnVwLHmSCNtyZZ6NhT29hTx5N1eDdRgjC7XAZeJyPsY4RZ6YmtPEbkII9RCRSrKHym6no2rnpBddU022dSW2VLXbKln1qASF1upGMPjdz9wB9DM3G7ltx6O4dwBRsqrB4ButvMzJYZUVtQz1e1p7jsGeMxcvhJjZPo/jHRQ1jGuhiyvrqeuZzbWVbelrquup/5TKrFxGpthTK1cZy6fAN5wMcrIiTrNPHYahnC1GzIrhhTZU8+GwrE9TTZgeNC9CdwMLARWK6UqwdOemRJiQdezcdUTsquuySab2jJb6pot9cwq4hIaReSXIjJGRFoow4PpWeAtjCToI0WkU5BTh2FkRrGMXtP64ciWejYUUbRnK4x4XFuBIRghFvqIyJGQ/u2p69m46gnZVddkk01tmS11zZZ6ZjPWlGrkJ4gIRtDp1wA3sAZjFHG9Umqnecxo4HzgG6XUK+a2FsBIjOnarcDvlWkPmI5kSz0biijbc75S6mVzW1vb/iKMdIq7U1CFiND1bFz1hOyqa7LJprbMlrpmSz01BlFpGkUkx5yGbY6RF3UcRtaC3RgjCgCUUl8C5UBfEWkpIoVKqX0YHsX3K6XOTGdBKlvq2VDE0J59zPZsppTaKSI55nRFZTp3KrqejauekF11TTbZ1JbZUtdsqafGS0RCo/nDPoDhDTwG6IN3yrUeuB441txn8S+gCPgEWC8inZRSnyil3ktoDRJIttSzoYizPWcAa8329IQuSkd0PRtXPSG76ppssqkts6Wu2VJPTSBhhUbzR1+AYYOwGrgPqAVOFJER4LE/uMf8s5gAXA18BwxQaZ7VIFvq2VAkoD0XkQHtqevZuOoJ2VXXZJNNbZktdc2WemqciSROoxt41GaHMAToDtwFPA0MEyPJ+BRgrIiUKCO+UhVwkjIyn2QC2VLPhiJb2lPXs3HVE7Krrskmm9oyW+qaLfXUOBDJ9PQC4C3x5oT8EiPu4GQgR0SuM0cVXTAyoJQDKKXey7CHI1vq2VBkS3vqejauekJ21TXZZFNbZktds6WeGgfCCo1KqYNKqWrljS94MkYgToDLgSPFSDz+OkasJcubKqPIlno2FNnSnrqeQCOqJ2RXXZNNNrVlttQ1W+qpcSbiNILmqEIB7YH3zc37gduA/sA6ZcRl8gS6zkSypZ4NRba0p65n46onZFddk002tWW21DVb6qnxJZqQO24gD9gJDDRHEncCbqXUbOvhaARkSz0bimxpT13PxlVPyK66JptsastsqWu21FNjI6rg3mIkHv/K/HtRKfV8sgqWSrKlng1FtrSnrmfjI5vqmmyyqS2zpa7ZUk+Nl2iFxi7AJcBflVLVSStVismWejYU2dKeup6Nj2yqa7LJprbMlrpmSz01XqJOI6jRaDQajUajyT6iSiOo0Wg0Go1Go8lOtNCo0Wg0Go1GowmLFho1Go1Go9FoNGHRQqNGo9FoNBqNJixaaNRoNBqNRqPRhEULjRqNptEiIsUicrW53ElE3k7ivQaLyOnJur5Go9GkGi00ajSaxkwxcDWAUmqLUupnSbzXYEALjRqNptGi4zRqNJpGi4i8AZwNrABWAUcqpfqLyGXAOUAzoDfwFyAfI1BxNXC6Umq3iPQEngLaAQeBXyullovIecDdQD2wFzgJWA00ATYDDwLrgMeAQuAQcLlSakUU9y4DFgFjgFzg/5RSXyejnTQajSYStKZRo9E0ZiYCa5RSg4E/+u3rD5wLHA38GTiolBoCzAF+aR7zLHCdUmoY8AfgH+b2u4BTlVKDgLOUUjXmtjeVUoOVUm8Cy4HjzWveBTwQ5b0Bmpplvxp4IZ6G0Gg0mnjJTXUBNBqNJkXMVErtB/aLyF7gf+b2JcBAESkCjgX+IyLWOQXm/y+BySLyFvBOkOu3BF4Skd6AAvIivbftuNcBlFKzRKSFiBQrpSpiq65Go9HEhxYaNRpNtmLPleu2rbsx+kYXUGFq+nxQSl0lIiOBCcACERnmcP37MITDn4hICVAWxb09t/K/dYj6aDQaTVLR09MajaYxsx9oHsuJSql9wDrTfhExGGQu91RKzVNK3QXsALo63Kslhn0jwGWxFZ8LzPsdB+xVSu2N8ToajUYTN1po1Gg0jRal1C7gSxFZCjwSwyUuBq4QkUXA9xhONQCPiMgS87pfYTiszASOEpHvROQC4GHgQRH5lthndarM858BrojxGhqNRpMQtPe0RqPRpCGm9/QflFLzU10WjUajAa1p1Gg0Go1Go9FEgNY0ajQajUaj0WjCojWNGo1Go9FoNJqwaKFRo9FoNBqNRhMWLTRqNBqNRqPRaMKihUaNRqPRaDQaTVi00KjRaDQajUajCcv/AxpBXA6OaEUJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 异常评分与真实值\n",
    "pred_score = model.predict_score(test_data_scaled)\n",
    "plot_anoms(origin_data = test_tsdata, predict_data=pred_score, feature_name = \"value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7、模型保存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型训练完成之后，可以将其保存到指定路径，方便后续使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('./anomaly_ae_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8、模型加载及推理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以加载已保存的模型用于新数据的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from paddlets.models.model_loader import load\n",
    "loaded_model = load('./anomaly_ae_model')\n",
    "pred_label = loaded_model.predict(test_data_scaled)\n",
    "pred_score = loaded_model.predict_score(test_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     label\n",
       "timestamp                 \n",
       "2013-12-14 16:55:00      0\n",
       "2013-12-14 17:00:00      0\n",
       "2013-12-14 17:05:00      0\n",
       "2013-12-14 17:10:00      0\n",
       "2013-12-14 17:15:00      0\n",
       "...                    ...\n",
       "2014-02-19 15:05:00      0\n",
       "2014-02-19 15:10:00      0\n",
       "2014-02-19 15:15:00      0\n",
       "2014-02-19 15:20:00      0\n",
       "2014-02-19 15:25:00      0\n",
       "\n",
       "[19279 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     label_score\n",
       "timestamp                       \n",
       "2013-12-14 16:55:00     0.004995\n",
       "2013-12-14 17:00:00     0.005670\n",
       "2013-12-14 17:05:00     0.011366\n",
       "2013-12-14 17:10:00     0.005947\n",
       "2013-12-14 17:15:00     0.005684\n",
       "...                          ...\n",
       "2014-02-19 15:05:00     0.005311\n",
       "2014-02-19 15:10:00     0.005450\n",
       "2014-02-19 15:15:00     0.005664\n",
       "2014-02-19 15:20:00     0.005371\n",
       "2014-02-19 15:25:00     0.007133\n",
       "\n",
       "[19279 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "验证加载的模型的预测结果，可以看到和之前的预测结果是一致的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anoms(origin_data=test_tsdata, predict_data=pred_label, feature_name=\"value\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
